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      Exploring the clinical and genetic associations of adult weight trajectories using electronic health records in a racially diverse biobank: a phenome-wide and polygenic risk study

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          Summary

          Background

          Weight trajectories might reflect individual health status. In this study, we aimed to examine the clinical and genetic associations of adult weight trajectories using electronic health records (EHRs) in the Bio Me Biobank.

          Methods

          We constructed four weight trajectories based on a-priori definitions of weight changes (5% or 10%) using annual weight in EHRs (stable weight, weight gain, weight loss, and weight cycle); the final weight dataset included 21 487 participants with 162 783 annual weight measures. To confirm accurate assignment of weight trajectories, we manually reviewed weight trajectory plots for 100 random individuals. We then did a hypothesis-free phenome-wide association study (PheWAS) to identify diseases associated with each weight trajectory. Next, we estimated the single-nucleotide polymorphism-based heritability (h SNP 2) of weight trajectories using GCTA-GREML, and we did a hypothesis-driven analysis of anorexia nervosa and depression polygenic risk scores (PRS) on these weight trajectories, given both diseases are associated with weight changes. We extended our analyses to the UK Biobank to replicate findings from a patient population to a generally healthy population.

          Findings

          We found high concordance between manually assigned weight trajectories and those assigned by the algorithm (accuracy ≥98%). Stable weight was consistently associated with lower disease risks among those passing Bonferroni-corrected p value in our PheWAS (p≤4·4 × 10 −5). Additionally, we identified an association between depression and weight cycle (odds ratio [OR] 1·42, 95% CI 1·31–1·55, p≤7·7 × 10 −16). The adult weight trajectories were heritable (using 5% weight change as the cutoff: h SNP 2 of 2·1%, 95% CI 0·9–3·3, for stable weight; 4·1%, 1·4–6·8, for weight gain; 5·5%, 2·8–8·2, for weight loss; and 4·7%, 2·3–7·1%, for weight cycle). Anorexia nervosa PRS was positively associated with weight loss trajectory among individuals without eating disorder diagnoses (OR 1SD 1·16, 95% CI 1·07–1·26, per 1 SD higher PRS, p=0·011), and the association was not attenuated by obesity PRS. No association was found between depression PRS and weight trajectories after permutation tests. All main findings were replicated in the UK Biobank (p<0·05).

          Interpretation

          Our findings suggest the importance of considering weight from a longitudinal aspect for its association with health and highlight a crucial role of weight management during disease development and progression.

          Funding

          Klarman Family Foundation, US National Institute of Mental Health (NIMH).

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          Most cited references29

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          GCTA: a tool for genome-wide complex trait analysis.

          For most human complex diseases and traits, SNPs identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability. Here we report a user-friendly software tool called genome-wide complex trait analysis (GCTA), which was developed based on a method we recently developed to address the "missing heritability" problem. GCTA estimates the variance explained by all the SNPs on a chromosome or on the whole genome for a complex trait rather than testing the association of any particular SNP to the trait. We introduce GCTA's five main functions: data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation. We focus on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation. The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets.
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            Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population

            Abstract The UK Biobank cohort is a population-based cohort of 500,000 participants recruited in the United Kingdom (UK) between 2006 and 2010. Approximately 9.2 million individuals aged 40–69 years who lived within 25 miles (40 km) of one of 22 assessment centers in England, Wales, and Scotland were invited to enter the cohort, and 5.5% participated in the baseline assessment. The representativeness of the UK Biobank cohort was investigated by comparing demographic characteristics between nonresponders and responders. Sociodemographic, physical, lifestyle, and health-related characteristics of the cohort were compared with nationally representative data sources. UK Biobank participants were more likely to be older, to be female, and to live in less socioeconomically deprived areas than nonparticipants. Compared with the general population, participants were less likely to be obese, to smoke, and to drink alcohol on a daily basis and had fewer self-reported health conditions. At age 70–74 years, rates of all-cause mortality and total cancer incidence were 46.2% and 11.8% lower, respectively, in men and 55.5% and 18.1% lower, respectively, in women than in the general population of the same age. UK Biobank is not representative of the sampling population; there is evidence of a “healthy volunteer” selection bias. Nonetheless, valid assessment of exposure-disease relationships may be widely generalizable and does not require participants to be representative of the population at large.
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              Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression

              Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened risk of suicide. We conducted a genome-wide association (GWA) meta-analysis based in 135,458 cases and 344,901 control, We identified 44 independent and significant loci. The genetic findings were associated with clinical features of major depression, and implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved in gene splicing were enriched for smaller association signal. We found important relations of genetic risk for major depression with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were putatively causal whereas major depression and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for major depression. These findings help refine and define the basis of major depression and imply a continuous measure of risk underlies the clinical phenotype.
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                Author and article information

                Contributors
                Journal
                101751302
                48799
                Lancet Digit Health
                Lancet Digit Health
                The Lancet. Digital health
                2589-7500
                12 August 2022
                August 2022
                30 June 2022
                27 October 2022
                : 4
                : 8
                : e604-e614
                Affiliations
                Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
                Department of Psychological Medicine, University of Otago, Christchurch, New Zealand
                Canterbury District Health Board, Christchurch, New Zealand
                Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
                Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
                Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
                InsideOut Institute, Charles Perkins Centre, The University of Sydney, Camperdown, Sydney, NSW, Australia
                Genetics & Computational Biology Department, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
                The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
                National Centre for Register-Based Research, Aarhus BSS, and Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
                The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
                National Centre for Register-Based Research, Aarhus BSS, and Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
                Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
                Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
                Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
                Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
                Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                Mental Illness Research, Education and Clinical Centers, James J Peters Department of Veterans Affairs Medical Center, Bronx, NY, USA
                Author notes

                Contributors

                JX and LMH conceptualised the study. JX created the method for weight trajectory classification, did the analysis, and drafted the manuscript. LMH advised on the development of the method and the analyses. RS cleaned and prepared the UK Biobank dataset for replication and drafted the methods section for UK Biobank. JSJ advised on the PheWAS-PRS code. JX and JSJ verified the underlying weight trajectory data. AB, JJ, MAK, ML, SLM, NGM, PBM, LVP, LMT, and CMB in the Eating Disorders Working Group of the Psychiatric Genomics Consortium contributed to the data collection for the anorexia nervosa GWAS summary statistics. JX, CMB, and LMH revised the manuscript. JX, JSJ, RS, and LMH have full access to all the study data. All authors were involved in reviewing the manuscript before submission. The corresponding author (LMH) had the full responsibility for the decision to submit the paper for publication.

                Correspondence to: Dr Laura M Huckins, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, laura.huckins@ 123456mssm.edu
                Article
                NIHMS1824966
                10.1016/S2589-7500(22)00099-1
                9612590
                35780037
                e6a1a012-6c20-4cf5-a7c3-11651b715b5a

                This is an Open Access article under the CC BY-NC-ND 4.0 license.

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